The invention relates to a device and to a method for determining the position of a vehicle on or above a planet surface, in particular in a transport route network on the planet surface. The term “vehicles” here is understood to mean aircraft, rail vehicles, ships, in particular motor vehicles (private cars, trucks, buses, etc.).
It is known that, for navigation purposes, today's vehicles are provided with satellite-based and/or inertia-based position determination systems.
Global navigation satellite systems or GNSS (English acronym) are systems for use on earth, in the air, or in a near-earth orbit for position determination and navigation by receiving the signals of navigation satellites. GNSS is a collective term for the use of existing and of future global satellite systems such as, for example, GPS (Global Positioning System), GLONASS (GLObal NAvigation Satellite System), GALILEO, or COMPASS). It is known that the satellite-based position determination systems are subject to errors (satellite position errors, time drift errors, ionosphere errors, troposphere errors, multipath-effect errors) that lead to inaccuracies in the determination of the position. These errors can be additive and enable a position determination with an accuracy of only 5 m to 150 m. Moreover, it is known that the signals of satellite position determination systems can be disturbed by interfering transmitters.
An inertial navigation system (English acronym INS) is a sensor system enabling the measurement of movements of bodies that move freely in space. Like the object to be monitored, the system also has a total of six kinematic degrees of freedom, of which three are translational and three are rotational, which are oriented relative to three unit vectors that are also orthogonal with respect to one another. Using this sensor system, the body coordinate system can be determined in real time and compared via a kinematic transformation to a fixed, previously known space coordinate system, which allows a use of the INS as navigation system. The main advantages here include that this navigation system can be operated without reference, and thus is also independent of any locating signals from the environment.
The term inertial navigation therefore is based on the fact that the acceleration and rotation rate sensors required for setting up an INS determine all the changes of the object position and orientation, based on accelerations that act on internally installed, quantitatively known masses (also referred to as seismic mass), which is based on the principle of mass inertia. The presence of a strong sensor drift, whose erroneous influence is cumulatively amplified during the course of a measurement, is an essential disadvantage of INS, primarily in the case of very low-priced sensors. In practice, one couples an INS with other navigational systems. For example, a combination with a Global Positioning System (GPS) yields absolute position indications at one second intervals, while the INS interpolates intermediate values. Such combined position determination systems can be found today in vehicles and aircraft. Inertia-based position determination systems are also subject to position errors (resulting particularly from the drift error in combination with, for example, the above-mentioned errors in GNSS systems).
However, inertial navigation is too expensive for mass use or use in private cars. Due to shadowing effects, multiple reception (reflections, multi-path) and disturbances (unintentional or also intentional caused by jamming transmitters) of the reception signal, the GNSS navigation does not always work in a problem-free and reliable way. Moreover, for example, the street coordinates in the available data sets (for example, from TomTom and Nokia “HERE,” formally Navteq) are much too imprecise to keep a vehicle in the lane in the case of autonomous steering and to enable drive control.
Moreover, a position determination based on visual landmarks with the aid of prominent and precisely surveyed landmarks such as, for example, the tips of church steeples, poles and prominent buildings, is known. Photographic views or image sequences taken from the vehicle are compared to a surrounding area image data bank, the landmarks (control points) are found, the angles are determined from which the landmarks are seen by the vehicle, and, using the known coordinates of the control points and triangulation, the position of the vehicle is determined. However, this method works only if the view is suitable. At this time, a data bank with the coordinates of control points that can be visually identified satisfactorily from the vehicle does not exist.
The vehicle positioning by GNSS locating is not sufficiently accurate, and, alone, it is not sufficiently failure-proof and reliable. In addition, the road data have not been acquired sufficiently precisely and do not contain objects/features for a more precise referencing. For high-precision vehicle guidance, for example, in the case of future autonomously controlled private cars, aircraft, ships, rail vehicles, the robust determination of the current vehicle position with high position accuracy must be possible. Thus, in particular, in motor vehicles that are moving autonomously, driver-assistance systems for automatically staying in lanes require a multiply redundant and secured sensor system for high-precision position determination in real time.